log using "~/Desktop/ISQ Replication/isq_blair_schwartz.log", replace

********************************************************************************
* 								 FULL ANALYSIS								   *
********************************************************************************

clear all

ssc install reghdfe, replace
ssc install ftools, replace
ssc install carryforward, replace
ssc install grstyle, replace
ssc install mediation, replace
ssc install outreg2, replace
ssc install coefplot, replace
ssc install ppmlhdfe, replace
ssc install cmp, replace
ssc install ghk2, replace
ssc install boottest, replace

set more off
set scheme plotplainblind
macro drop _all
est drop _all
set matsize 800
set seed 8675309

** Set Working Directory

if c(username) == "christopherblair"{
global dir "~/Desktop/ISQ Replication"
global data "${dir}/data"
global code "${dir}/code"										
global results "${dir}/results"										
}

cd "${dir}"

if c(username) == "cb2257"{
global dir "~/Desktop/ISQ Replication"
global data "${dir}/data"
global code "${dir}/code"										
global results "${dir}/results"										
}

cd "${dir}"

else if c(username) == "youruser"{
global dir "~/Desktop/ISQ Replication"
}

cd "${dir}"

********************************************************************************

use "${data}/leader_figure.dta", clear
est drop _all

********************************************************************************
*									 MAKE FIGURE 1							   *
********************************************************************************

do "${code}/GenderPeace_Leader.do"

********************************************************************************
********************************************************************************
********************************** STUDY 1 *************************************
********************************************************************************
********************************************************************************

import delimited "${data}/study1_main.csv", clear
est drop _all

********************************************************************************
*								 CLEAN STUDY 1 DATA							   *
********************************************************************************

do "${code}/GenderPeace_Cleaning1.do"

********************************************************************************
*						 FIGURE 2: STUDY 1 PREMIA							   *
********************************************************************************

***Main Effect of Gender -- Outcome 1, Binary DV
reg disapproval1_binary femsq femconc malesq maleconc, robust noconst
lincom femconc-femsq-maleconc+malesq, level(95)
lincom femconc-femsq-maleconc+malesq, level(90)

matrix study1_gender = J(1,5,.)
matrix colnames study1_gender = premia ll95 ul95 ll90 ul90
matrix rownames study1_gender = gender
matrix study1_gender[1, 1] = .116102*100
matrix study1_gender[1, 2] = .0113605*100
matrix study1_gender[1, 3] = .2208434*100
matrix study1_gender[1, 4] = .0282283*100
matrix study1_gender[1, 5] = .2039757*100
matrix list study1_gender

*** Main Effect of Partisanship -- Outcome 1, Binary DV:
reg disapproval1_binary demsq demconc repsq repconc, robust noconst
lincom demconc-demsq-repconc+repsq, level(95)
lincom demconc-demsq-repconc+repsq, level(90)

matrix study1_partisan = J(1,5,.)
matrix colnames study1_partisan = premia ll95 ul95 ll90 ul90
matrix rownames study1_partisan = partisan
matrix study1_partisan[1, 1] = .0672579*100
matrix study1_partisan[1, 2] = -.0377029*100
matrix study1_partisan[1, 3] = .1722187*100
matrix study1_partisan[1, 4] = -.0207998*100
matrix study1_partisan[1, 5] = .1553155*100
matrix list study1_partisan

***Create Figure 

coefplot (matrix(study1_gender[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabcolor(black) mlabposition(12) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_partisan[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))), legend(off) ylabel(1 `" "Gendered" "Peace Premium" "' 2  `" "Partisan" "Peace Premium" "', labsize(medium)) xlabel(-10(5)25, labsize(medium)) xmtick(-10(1)25) xtitle("Peace Premia (in % Points)", size(medlarge)) xline(0, lcolor(cranberry) lpatt(solid)) xvarformat(%4.1f)
graph export "${results}/figure2.eps", replace

eststo clear

********************************************************************************
*						 FIGURE 3: STUDY 1 MECHANISMS						   *
********************************************************************************

*** Gender -- Best Strategy
reg beststrategy1_binary femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study1_g_strategy = J(1,5,.)
matrix colnames study1_g_strategy = premia ll95 ul95 ll90 ul90
matrix rownames study1_g_strategy = gender
matrix study1_g_strategy[1, 1] = .1119976*100
matrix study1_g_strategy[1, 2] = -.0163926*100
matrix study1_g_strategy[1, 3] = .2403878*100
matrix study1_g_strategy[1, 4] = .0042861*100
matrix study1_g_strategy[1, 5] = .219709*100
matrix list study1_g_strategy

*** Party -- Best Strategy
reg beststrategy1_binary demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study1_p_strategy = J(1,5,.)
matrix colnames study1_p_strategy = premia ll95 ul95 ll90 ul90
matrix rownames study1_p_strategy = partisan
matrix study1_p_strategy[1, 1] = .0162623*100
matrix study1_p_strategy[1, 2] = -.1123725*100
matrix study1_p_strategy[1, 3] = .1448971*100
matrix study1_p_strategy[1, 4] = -.0916543*100
matrix study1_p_strategy[1, 5] = .124179*100
matrix list study1_p_strategy

*** Gender -- Competent
reg competent1_binary femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study1_g_competent = J(1,5,.)
matrix colnames study1_g_competent = premia ll95 ul95 ll90 ul90
matrix rownames study1_g_competent = gender
matrix study1_g_competent[1, 1] = .1715333*100
matrix study1_g_competent[1, 2] = .0420628*100
matrix study1_g_competent[1, 3] = .3010039*100
matrix study1_g_competent[1, 4] = .0629155*100
matrix study1_g_competent[1, 5] = .2801511*100
matrix list study1_g_competent

*** Party -- Competent
reg competent1_binary demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study1_p_competent = J(1,5,.)
matrix colnames study1_p_competent = premia ll95 ul95 ll90 ul90
matrix rownames study1_p_competent = partisan
matrix study1_p_competent[1, 1] = -.0101035*100
matrix study1_p_competent[1, 2] = -.1399383*100
matrix study1_p_competent[1, 3] = .1197312*100
matrix study1_p_competent[1, 4] = -.1190269*100
matrix study1_p_competent[1, 5] = .0988198*100
matrix list study1_p_competent

*** Gender -- Moderate
reg moderate femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study1_g_moderate = J(1,5,.)
matrix colnames study1_g_moderate = premia ll95 ul95 ll90 ul90
matrix rownames study1_g_moderate = gender
matrix study1_g_moderate[1, 1] = .0336674*100
matrix study1_g_moderate[1, 2] = -.0723408*100
matrix study1_g_moderate[1, 3] = .1396756*100
matrix study1_g_moderate[1, 4] = -.0552669*100
matrix study1_g_moderate[1, 5] = .1226018*100
matrix list study1_g_moderate

*** Party -- Moderate
reg moderate demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study1_p_moderate = J(1,5,.)
matrix colnames study1_p_moderate = premia ll95 ul95 ll90 ul90
matrix rownames study1_p_moderate = partisan
matrix study1_p_moderate[1, 1] = .0027443*100
matrix study1_p_moderate[1, 2] = -.1032401*100
matrix study1_p_moderate[1, 3] = .1087288*100
matrix study1_p_moderate[1, 4] = -.0861701*100
matrix study1_p_moderate[1, 5] = .0916587*100
matrix list study1_p_moderate

*** Gender -- Trustworthy
reg trustworthy1_binary femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study1_g_trust = J(1,5,.)
matrix colnames study1_g_trust = premia ll95 ul95 ll90 ul90
matrix rownames study1_g_trust = gender
matrix study1_g_trust[1, 1] = .0588489*100
matrix study1_g_trust[1, 2] = -.0744756*100
matrix study1_g_trust[1, 3] = .1921734*100
matrix study1_g_trust[1, 4] = -.0530022*100
matrix study1_g_trust[1, 5] = .1706999*100
matrix list study1_g_trust

*** Party -- Trustworthy
reg trustworthy1_binary demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study1_p_trust = J(1,5,.)
matrix colnames study1_p_trust = premia ll95 ul95 ll90 ul90
matrix rownames study1_p_trust = partisan
matrix study1_p_trust[1, 1] = -.0139167*100
matrix study1_p_trust[1, 2] = -.1470694*100
matrix study1_p_trust[1, 3] = .1192361*100
matrix study1_p_trust[1, 4] = -.1256236*100
matrix study1_p_trust[1, 5] = .0977903*100
matrix list study1_p_trust

*** Gender -- Tough
reg tough1_binary femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study1_g_tough = J(1,5,.)
matrix colnames study1_g_tough = premia ll95 ul95 ll90 ul90
matrix rownames study1_g_tough = gender
matrix study1_g_tough[1, 1] = .0410277*100
matrix study1_g_tough[1, 2] = -.0833094*100
matrix study1_g_tough[1, 3] = .1653648*100
matrix study1_g_tough[1, 4] = -.0632835*100
matrix study1_g_tough[1, 5] = .1453389*100
matrix list study1_g_tough

*** Party -- Tough
reg tough1_binary demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study1_p_tough = J(1,5,.)
matrix colnames study1_p_tough = premia ll95 ul95 ll90 ul90
matrix rownames study1_p_tough = partisan
matrix study1_p_tough[1, 1] = .0934841*100
matrix study1_p_tough[1, 2] = -.0307631*100
matrix study1_p_tough[1, 3] = .2177313*100
matrix study1_p_tough[1, 4] = -.0107516*100
matrix study1_p_tough[1, 5] = .1977198*100
matrix list study1_p_tough

coefplot (matrix(study1_g_strategy[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_g_competent[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_g_moderate[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_g_trust[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_g_tough[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))), legend(off) ylabel(.67 "Policy Credibility" .83  "Competence" 1  "Moderation" 1.17  "Trustworthiness" 1.33  "Toughness", labsize(medsmall)) xlabel(-20(5)35, labsize(medium)) xmtick(-20(1)35) xtitle("Gendered Peace Premium" "(in % Points)", size(medlarge)) xline(0, lcolor(cranberry) lpatt(solid)) xvarformat(%4.1f)
graph export "${results}/figure3a.eps", replace

coefplot (matrix(study1_p_strategy[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_p_competent[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(11) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_p_moderate[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(1) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_p_trust[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(11) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study1_p_tough[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))), legend(off) ylabel(.67 "Policy Credibility" .83  "Competence" 1  "Moderation" 1.17  "Trustworthiness" 1.33  "Toughness", labsize(medsmall)) xlabel(-20(5)35, labsize(medium)) xmtick(-20(1)35) xtitle("Partisan Peace Premium" "(in % Points)", size(medlarge)) xline(0, lcolor(cranberry) lpatt(solid)) xvarformat(%4.1f)
graph export "${results}/figure3b.eps", replace

eststo clear

********************************************************************************
*							 TABLE 1: STUDY 1 SUCCESS						   *
********************************************************************************

*Binary DV
reg disapproval2_binary femsq femconc malesq maleconc, robust noconst
lincom femconc-femsq-maleconc+malesq

*Full DV
reg disapproval2 femsq femconc malesq maleconc, robust noconst
lincom femconc-femsq-maleconc+malesq	
	
*Passed Manipulation Check / Binary DV
reg disapproval2_binary femsq femconc malesq maleconc if policy_manipcheck==1 & name_manipcheck==1, robust noconst
lincom femconc-femsq-maleconc+malesq

*Passed Manipulation Check / Full DV
reg disapproval2 femsq femconc malesq maleconc if policy_manipcheck==1 & name_manipcheck==1, robust noconst
lincom femconc-femsq-maleconc+malesq	

********************************************************************************
*					 TABLE A-1: STUDY 1 7-POINT SCALE						   *
********************************************************************************

***Main Effect of Gender -- Outcome 1, Full DV
reg disapproval1 femsq femconc malesq maleconc, robust noconst
lincom femconc-femsq
lincom maleconc-malesq
lincom femconc-femsq-maleconc+malesq

*** Main Effect of Partisanship -- Outcome 1, Full DV
reg disapproval1 demsq demconc repsq repconc, robust noconst
lincom demconc-demsq
lincom repconc-repsq
lincom demconc-demsq-repconc+repsq

********************************************************************************
*					 TABLE A-2: STUDY 1 MANIPULATION CHECK					   *
********************************************************************************

***Main Effect of Gender -- Outcome 1, Binary DV
reg disapproval1_binary femsq femconc malesq maleconc if policy_manipcheck==1 & name_manipcheck==1, robust noconst
lincom femconc-femsq
lincom maleconc-malesq
lincom femconc-femsq-maleconc+malesq

*** Main Effect of Partisanship -- Outcome 1, Binary DV
reg disapproval1_binary demsq demconc repsq repconc if policy_manipcheck==1, robust noconst
lincom demconc-demsq
lincom repconc-repsq
lincom demconc-demsq-repconc+repsq

********************************************************************************
*						 TABLE A-3: STUDY 1 WITH COVARIATES					   *
********************************************************************************

eststo clear

*Model 1: Main Effect of Gender -- Outcome 1, Binary DV
eststo: reg disapproval1_binary femsq femconc malesq maleconc democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom femconc-femsq-maleconc+malesq
	
*Model 2: Main Effect of Gender -- Outcome 1, Full DV
eststo: reg disapproval1 femsq femconc malesq maleconc democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom femconc-femsq-maleconc+malesq

*Model 3: Main Effect of Partisanship -- Outcome 1, Binary DV
eststo: reg disapproval1_binary demsq demconc repsq repconc female hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom demconc-demsq-repconc+repsq
	
*Model 4: Main Effect of Partisanship -- Outcome 1, Full DV
eststo: reg disapproval1 demsq demconc repsq repconc female hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom demconc-demsq-repconc+repsq

esttab using "${results}/table_a3.tex", cells(b(fmt(3)) ci(fmt(3) par)) noeqlines eqlabels(none) nogaps se varlabels(demsq "Democratic x Status Quo" demconc "Democratic x Conciliatory" repsq "Republican x Status Quo" repconc "Republican x Conciliatory" femsq "Female x Status Quo" femconc "Female x Conciliatory" malesq "Male x Status Quo" maleconc "Male x Conciliatory" democrat "Democratic President" female "Female President") keep(malesq maleconc femsq femconc repsq repconc demsq demconc female democrat) order(malesq maleconc femsq femconc repsq repconc demsq demconc female democrat) label star(* 0.10 ** 0.05 *** .01) nonotes mtitle("Disapproval (Binary)" "Disapproval (7-Point)" "Disapproval (Binary)" "Disapproval (7-Point)") b(3) se(3) replace

eststo clear

********************************************************************************
*				 TABLE A-4: STUDY 1 MEDIATION CREDIBILITY					   *
********************************************************************************

eststo clear

***Male
medeff (regress beststrategy1 conciliatory democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 beststrategy1 conciliatory democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if male==1, mediate(beststrategy1) treat(conciliatory) sims(2000) seed(8675309)

***Female	
medeff (regress beststrategy1 conciliatory democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 beststrategy1 conciliatory democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if female==1, mediate(beststrategy1) treat(conciliatory) sims(2000) seed(8675309)


********************************************************************************
*				 TABLE A-5: STUDY 1 MEDIATION COMPETENCE					   *
********************************************************************************

eststo clear

***Male
medeff (regress competent1 conciliatory democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 competent1 conciliatory democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if male==1, mediate(competent1) treat(conciliatory) sims(2000) seed(8675309)

***Female	
medeff (regress competent1 conciliatory democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 competent1 conciliatory democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if female==1, mediate(competent1) treat(conciliatory) sims(2000) seed(8675309)


********************************************************************************
*						 TABLE 2: STUDY 1 REPUBLICANS						   *
********************************************************************************

eststo clear

***Model 1: Binary DV, Full Sample
reg disapproval1_binary 1.femsq 1.femsq#republican_respondent 1.femconc 1.femconc#republican_respondent 1.malesq 1.malesq#republican_respondent 1.maleconc 1.maleconc#republican_respondent, robust noconst

*Gendered Peace Premium (Republican Respondent)
lincom (1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent)

*Gendered Peace Premium (Non-Republican Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 2: 7-Point DV, Full Sample
reg disapproval1 1.femsq 1.femsq#republican_respondent 1.femconc 1.femconc#republican_respondent 1.malesq 1.malesq#republican_respondent 1.maleconc 1.maleconc#republican_respondent, robust noconst

*Gendered Peace Premium (Republican Respondent)
lincom (1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent)

*Gendered Peace Premium (Non-Republican Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 3: Binary DV, Attentive Sample
reg disapproval1_binary 1.femsq 1.femsq#republican_respondent 1.femconc 1.femconc#republican_respondent 1.malesq 1.malesq#republican_respondent 1.maleconc 1.maleconc#republican_respondent if policy_manipcheck==1 & name_manipcheck==1, robust noconst

*Gendered Peace Premium (Republican Respondent)
lincom (1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent)

*Gendered Peace Premium (Non-Republican Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 4: 7-Point DV, Attentive Sample
reg disapproval1 1.femsq 1.femsq#republican_respondent 1.femconc 1.femconc#republican_respondent 1.malesq 1.malesq#republican_respondent 1.maleconc 1.maleconc#republican_respondent if policy_manipcheck==1 & name_manipcheck==1, robust noconst

*Gendered Peace Premium (Republican Respondent)
lincom (1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent)

*Gendered Peace Premium (Non-Republican Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


********************************************************************************
*				 TABLE A-6: STUDY 1 HETEROGENEOUS EFFECTS					   *
********************************************************************************

eststo clear

***Model 1: Republican Respondent
eststo: reg disapproval1 1.femsq 1.femsq#republican_respondent 1.femconc 1.femconc#republican_respondent 1.malesq 1.malesq#republican_respondent 1.maleconc 1.maleconc#republican_respondent democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Republican Respondent)
lincom (1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent)

*Gendered Peace Premium (Non-Republican Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 2: Hostile Sexism 
eststo: reg disapproval1 1.femsq 1.femsq#hostsexismIQR 1.femconc 1.femconc#hostsexismIQR 1.malesq 1.malesq#hostsexismIQR 1.maleconc 1.maleconc#hostsexismIQR democrat republican_respondent benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Sexist Respondent)
lincom (1.femconc+1.femconc#1.hostsexismIQR)-(1.femsq+1.femsq#1.hostsexismIQR)-(1.maleconc+1.maleconc#1.hostsexismIQR)+(1.malesq+1.malesq#1.hostsexismIQR)

*Gendered Peace Premium (Non-Sexist Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.hostsexismIQR)-(1.femsq+1.femsq#1.hostsexismIQR)-(1.maleconc+1.maleconc#1.hostsexismIQR)+(1.malesq+1.malesq#1.hostsexismIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 3: Benevolent Sexism 
eststo: reg disapproval1 1.femsq 1.femsq#benevsexismIQR 1.femconc 1.femconc#benevsexismIQR 1.malesq 1.malesq#benevsexismIQR 1.maleconc 1.maleconc#benevsexismIQR democrat republican_respondent hostsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Sexist Respondent)
lincom (1.femconc+1.femconc#1.benevsexismIQR)-(1.femsq+1.femsq#1.benevsexismIQR)-(1.maleconc+1.maleconc#1.benevsexismIQR)+(1.malesq+1.malesq#1.benevsexismIQR)

*Gendered Peace Premium (Non-Sexist Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.benevsexismIQR)-(1.femsq+1.femsq#1.benevsexismIQR)-(1.maleconc+1.maleconc#1.benevsexismIQR)+(1.malesq+1.malesq#1.benevsexismIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 4: Second-Order Sexism 
eststo: reg disapproval1 1.femsq 1.femsq#secordersexismIQR 1.femconc 1.femconc#secordersexismIQR 1.malesq 1.malesq#secordersexismIQR 1.maleconc 1.maleconc#secordersexismIQR democrat republican_respondent hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Sexist Respondent)
lincom (1.femconc+1.femconc#1.secordersexismIQR)-(1.femsq+1.femsq#1.secordersexismIQR)-(1.maleconc+1.maleconc#1.secordersexismIQR)+(1.malesq+1.malesq#1.secordersexismIQR)

*Gendered Peace Premium (Non-Sexist Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.secordersexismIQR)-(1.femsq+1.femsq#1.secordersexismIQR)-(1.maleconc+1.maleconc#1.secordersexismIQR)+(1.malesq+1.malesq#1.secordersexismIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 5: Militant Assertiveness 
eststo: reg disapproval1 1.femsq 1.femsq#hawkishIQR 1.femconc 1.femconc#hawkishIQR 1.malesq 1.malesq#hawkishIQR 1.maleconc 1.maleconc#hawkishIQR democrat republican_respondent hostsexism benevsexism secordersexism female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Hawkish Respondent)
lincom (1.femconc+1.femconc#1.hawkishIQR)-(1.femsq+1.femsq#1.hawkishIQR)-(1.maleconc+1.maleconc#1.hawkishIQR)+(1.malesq+1.malesq#1.hawkishIQR)

*Gendered Peace Premium (Non-Hawkish Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.hawkishIQR)-(1.femsq+1.femsq#1.hawkishIQR)-(1.maleconc+1.maleconc#1.hawkishIQR)+(1.malesq+1.malesq#1.hawkishIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 6: Education 
eststo: reg disapproval1 1.femsq 1.femsq#educationIQR 1.femconc 1.femconc#educationIQR 1.malesq 1.malesq#educationIQR 1.maleconc 1.maleconc#educationIQR democrat republican_respondent hostsexism benevsexism secordersexism hawkish female_respondent political_identfication hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Educated Respondent)
lincom (1.femconc+1.femconc#1.educationIQR)-(1.femsq+1.femsq#1.educationIQR)-(1.maleconc+1.maleconc#1.educationIQR)+(1.malesq+1.malesq#1.educationIQR)

*Gendered Peace Premium (Uneducated Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.educationIQR)-(1.femsq+1.femsq#1.educationIQR)-(1.maleconc+1.maleconc#1.educationIQR)+(1.malesq+1.malesq#1.educationIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 7: Female Respondents
eststo: reg disapproval1 1.femsq 1.femsq#female_respondent 1.femconc 1.femconc#female_respondent 1.malesq 1.malesq#female_respondent 1.maleconc 1.maleconc#female_respondent democrat republican_respondent hostsexism benevsexism secordersexism hawkish political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Female Respondent)
lincom (1.femconc+1.femconc#1.female_respondent)-(1.femsq+1.femsq#1.female_respondent)-(1.maleconc+1.maleconc#1.female_respondent)+(1.malesq+1.malesq#1.female_respondent)

*Gendered Peace Premium (Male Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.female_respondent)-(1.femsq+1.femsq#1.female_respondent)-(1.maleconc+1.maleconc#1.female_respondent)+(1.malesq+1.malesq#1.female_respondent))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

********************************************************************************
*				 TABLE A-7: STUDY 1 CO- AND OUT-PARTISANS					   *
********************************************************************************

eststo clear

***Model 1: Co-Partisans

reg disapproval1 femsq femconc malesq maleconc democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo if in_partisan==1, robust noconst
lincom femconc-femsq-maleconc+malesq

***Model 2: Out-Partisans

reg disapproval1 femsq femconc malesq maleconc democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo if out_partisan==1, robust noconst
lincom femconc-femsq-maleconc+malesq

***Model 3: Co- vs. Out-Partisans

eststo: reg disapproval1 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat republican_respondent hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Out-Partisan)
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

*Gendered Peace Premium (Co-Partisan)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


********************************************************************************
*					 TABLE A-8: STUDY 1 GENDER X DEM PRESIDENT				   *
********************************************************************************

eststo clear

***Model 1: Binary DV, Full Sample
eststo: reg disapproval1_binary 1.femsq 1.femsq#democrat 1.femconc 1.femconc#democrat 1.malesq 1.malesq#democrat 1.maleconc 1.maleconc#democrat, robust noconst

*Gendered Peace Premium (Democratic President)
lincom (1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat)

*Gendered Peace Premium (Non-Democratic President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 2: 7-Point DV, Full Sample
eststo: reg disapproval1 1.femsq 1.femsq#democrat 1.femconc 1.femconc#democrat 1.malesq 1.malesq#democrat 1.maleconc 1.maleconc#democrat, robust noconst

*Gendered Peace Premium (Democratic President)
lincom (1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat)

*Gendered Peace Premium (Non-Democratic President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 3: Binary DV, Attentive Sample
eststo: reg disapproval1_binary 1.femsq 1.femsq#democrat 1.femconc 1.femconc#democrat 1.malesq 1.malesq#democrat 1.maleconc 1.maleconc#democrat if policy_manipcheck==1 & name_manipcheck==1, robust noconst

*Gendered Peace Premium (Democratic President)
lincom (1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat)

*Gendered Peace Premium (Non-Democratic President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 4: 7-Point DV, Attentive Sample
eststo: reg disapproval1 1.femsq 1.femsq#democrat 1.femconc 1.femconc#democrat 1.malesq 1.malesq#democrat 1.maleconc 1.maleconc#democrat if policy_manipcheck==1 & name_manipcheck==1, robust noconst

*Gendered Peace Premium (Democratic President)
lincom (1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat)

*Gendered Peace Premium (Non-Democratic President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


********************************************************************************
*					 TABLE A-9: STUDY 1 OTHER HETEROGENEITY				   *
********************************************************************************

eststo clear

***Model 1: Sexists and Women Leaders

eststo: regress disapproval1 i.female##c.hostsexism conciliatory democrat benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust
eststo clear

***Model 2: Hawks and Conciliation

eststo: regress disapproval1 i.conciliatory##c.hawkish female democrat hostsexism benevsexism secordersexism female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust
eststo clear

***Model 3: Republicans and Conciliation

eststo: regress disapproval1 i.conciliatory##c.political_identfication female democrat hostsexism benevsexism secordersexism hawkish female_respondent education hhi age white SexismOrder nonwhite_placebo, robust
eststo clear

***Model 4: Women and Women Leaders

eststo: regress disapproval1 i.female##i.female_respondent conciliatory democrat hostsexism benevsexism secordersexism hawkish political_identfication education hhi age white SexismOrder nonwhite_placebo, robust
eststo clear

clear 


********************************************************************************
********************************************************************************
********************************** STUDY 2 *************************************
********************************************************************************
********************************************************************************

import delimited "${data}/study2_main.csv", clear
est drop _all

********************************************************************************
*								 CLEAN STUDY 2 DATA							   *
********************************************************************************

do "${code}/GenderPeace_Cleaning2.do"

********************************************************************************
*						 FIGURE 4: STUDY 2 PREMIA							   *
********************************************************************************

***Main Effect of Disposition -- Outcome 1, Binary DV
reg disapproval1_binary hawksq hawkconc dovesq doveconc, robust noconst
lincom doveconc-dovesq-hawkconc+hawksq, level(95)
lincom doveconc-dovesq-hawkconc+hawksq, level(90)

matrix study2_dispo = J(1,5,.)
matrix colnames study2_dispo = premia ll95 ul95 ll90 ul90
matrix rownames study2_dispo = disposition
matrix study2_dispo[1, 1] = .1276018*100
matrix study2_dispo[1, 2] = .0613679*100
matrix study2_dispo[1, 3] = .1938358*100
matrix study2_dispo[1, 4] = .0720239*100
matrix study2_dispo[1, 5] = .1831797*100
matrix list study2_dispo

***Main Effect of Gender -- Outcome 1, Binary DV
reg disapproval1_binary femsq femconc malesq maleconc, robust noconst
lincom femconc-femsq-maleconc+malesq, level(95)
lincom femconc-femsq-maleconc+malesq, level(90)

matrix study2_gender = J(1,5,.)
matrix colnames study2_gender = premia ll95 ul95 ll90 ul90
matrix rownames study2_gender = gender
matrix study2_gender[1, 1] = .0258541*100
matrix study2_gender[1, 2] = -.0408563*100
matrix study2_gender[1, 3] = .0925645*100
matrix study2_gender[1, 4] = -.0301236*100
matrix study2_gender[1, 5] = .0818318*100
matrix list study2_gender

*** Main Effect of Partisanship -- Outcome 1, Binary DV:
reg disapproval1_binary demsq demconc repsq repconc, robust noconst
lincom demconc-demsq-repconc+repsq, level(95)
lincom demconc-demsq-repconc+repsq, level(90)

matrix study2_partisan = J(1,5,.)
matrix colnames study2_partisan = premia ll95 ul95 ll90 ul90
matrix rownames study2_partisan = partisan
matrix study2_partisan[1, 1] = .0444943*100
matrix study2_partisan[1, 2] = -.0222402*100
matrix study2_partisan[1, 3] = .1112288*100
matrix study2_partisan[1, 4] =  -.0115036*100
matrix study2_partisan[1, 5] = .1004922*100
matrix list study2_partisan

***Create Figure 

coefplot (matrix(study2_gender[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabcolor(black) mlabposition(12) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_partisan[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_dispo[,1]), ci((2 3) (4 5)) msymbol(T) msize(large) mfcolor(white) mlcolor(black) mlabel mlabcolor(black) mlabposition(12) ciopts(lcolor(black black) lwidth(.55 1.1))), legend(off) ylabel(1 `" "Gendered" "Peace Premium" "' 2  `" "Partisan" "Peace Premium" "' 3 `" "Dispositional" "Peace Premium" "', labsize(medium)) xlabel(-10(5)25, labsize(medium)) xmtick(-10(1)25) xtitle("Peace Premia (in % Points)", size(medlarge)) xline(0, lcolor(cranberry) lpatt(solid)) xvarformat(%4.1f)
graph export "${results}/figure4.eps", replace

eststo clear

********************************************************************************
*					 TABLE 3: STUDY 2 CO- AND OUT-PARTISANS					   *
********************************************************************************

************ Gender -- Binary DV ************

reg disapproval1_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Out-Partisan)
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

*Gendered Peace Premium (Co-Partisan)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

************ Gender -- Full DV ************

reg disapproval1 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Out-Partisan)
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

*Gendered Peace Premium (Co-Partisan)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

************ Gender -- Passed Manipulation Check / Binary DV ************

reg disapproval1_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo if policy_manipcheck==1 & name_manipcheck==1, robust noconst

*Gendered Peace Premium (Out-Partisan)
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

*Gendered Peace Premium (Co-Partisan)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

************ Gender -- Passed Manipulation Check / Full DV ************

reg disapproval1 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo if policy_manipcheck==1 & name_manipcheck==1, robust noconst

*Gendered Peace Premium (Out-Partisan)
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

*Gendered Peace Premium (Co-Partisan)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

eststo clear

************ Gender -- IN-TEXT CALCULATION ************

reg disapproval1_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo if policy_manipcheck==1 & name_manipcheck==1 & democratic_respondent==1, robust noconst
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

reg disapproval1_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo if policy_manipcheck==1 & name_manipcheck==1 & republican_respondent==1, robust noconst
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

ttesti 438 .04771 2.5367353 565 .1728215 2.3611042

********************************************************************************
*						 FIGURE 5: STUDY 2 MECHANISMS						   *
********************************************************************************

*** Gender -- Best Strategy
reg beststrategy1_binary femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study2_g_strategy = J(1,5,.)
matrix colnames study2_g_strategy = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_strategy = gender
matrix study2_g_strategy[1, 1] = .0401057*100
matrix study2_g_strategy[1, 2] = -.0446744*100
matrix study2_g_strategy[1, 3] = .1248858*100
matrix study2_g_strategy[1, 4] = -.031034*100
matrix study2_g_strategy[1, 5] = .1112454*100
matrix list study2_g_strategy

*** Party -- Best Strategy
reg beststrategy1_binary demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study2_p_strategy = J(1,5,.)
matrix colnames study2_p_strategy = premia ll95 ul95 ll90 ul90
matrix rownames study2_p_strategy = partisan
matrix study2_p_strategy[1, 1] = .0555593*100
matrix study2_p_strategy[1, 2] = -.0291724*100
matrix study2_p_strategy[1, 3] = .140291*100
matrix study2_p_strategy[1, 4] = -.0155398 *100
matrix study2_p_strategy[1, 5] = .1266584*100
matrix list study2_p_strategy

*** Disposition -- Best Strategy
reg beststrategy1_binary dovesq doveconc hawksq hawkconc, robust noconst
lincom hawkconc-hawksq-doveconc+dovesq, level(95)
lincom hawkconc-hawksq-doveconc+dovesq, level(90)

matrix study2_d_strategy = J(1,5,.)
matrix colnames study2_d_strategy = premia ll95 ul95 ll90 ul90
matrix rownames study2_d_strategy = disposition
matrix study2_d_strategy[1, 1] = .1467937*100
matrix study2_d_strategy[1, 2] = .0625459*100
matrix study2_d_strategy[1, 3] = .2310414*100
matrix study2_d_strategy[1, 4] = .0761007*100
matrix study2_d_strategy[1, 5] = .2174867*100
matrix list study2_d_strategy

*** Gender -- Competent
reg competent1_binary femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study2_g_competent = J(1,5,.)
matrix colnames study2_g_competent = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_competent = gender
matrix study2_g_competent[1, 1] = .0378144*100
matrix study2_g_competent[1, 2] = -.045811*100
matrix study2_g_competent[1, 3] = .1214398*100
matrix study2_g_competent[1, 4] = -.0323564*100
matrix study2_g_competent[1, 5] = .1079852*100
matrix list study2_g_competent

*** Party -- Competent
reg competent1_binary demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study2_p_competent = J(1,5,.)
matrix colnames study2_p_competent = premia ll95 ul95 ll90 ul90
matrix rownames study2_p_competent = partisan
matrix study2_p_competent[1, 1] = .0168342*100
matrix study2_p_competent[1, 2] = -.0667171*100
matrix study2_p_competent[1, 3] = .1003856*100
matrix study2_p_competent[1, 4] = -.0532743*100
matrix study2_p_competent[1, 5] = .0869428*100
matrix list study2_p_competent

*** Disposition -- Competent
reg competent1_binary dovesq doveconc hawksq hawkconc, robust noconst
lincom hawkconc-hawksq-doveconc+dovesq, level(95)
lincom hawkconc-hawksq-doveconc+dovesq, level(90)

matrix study2_d_competent = J(1,5,.)
matrix colnames study2_d_competent = premia ll95 ul95 ll90 ul90
matrix rownames study2_d_competent = disposition
matrix study2_d_competent[1, 1] = .0561345*100
matrix study2_d_competent[1, 2] = -.0271773*100
matrix study2_d_competent[1, 3] = .1394463*100
matrix study2_d_competent[1, 4] = -.0137731*100
matrix study2_d_competent[1, 5] = .1260421*100
matrix list study2_d_competent

*** Gender -- Moderate
reg moderate femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study2_g_moderate = J(1,5,.)
matrix colnames study2_g_moderate = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_moderate = gender
matrix study2_g_moderate[1, 1] = .0118417*100
matrix study2_g_moderate[1, 2] = -.0487869*100
matrix study2_g_moderate[1, 3] = .0724703*100
matrix study2_g_moderate[1, 4] = -.0390322*100
matrix study2_g_moderate[1, 5] = .0627157*100
matrix list study2_g_moderate

*** Party -- Moderate
reg moderate demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study2_p_moderate = J(1,5,.)
matrix colnames study2_p_moderate = premia ll95 ul95 ll90 ul90
matrix rownames study2_p_moderate = partisan
matrix study2_p_moderate[1, 1] = -.0111827*100
matrix study2_p_moderate[1, 2] = -.0716557*100
matrix study2_p_moderate[1, 3] = .0492904*100
matrix study2_p_moderate[1, 4] = -.0619261*100
matrix study2_p_moderate[1, 5] = .0395608*100
matrix list study2_p_moderate

*** Disposition -- Moderate
reg moderate dovesq doveconc hawksq hawkconc, robust noconst
lincom hawkconc-hawksq-doveconc+dovesq, level(95)
lincom hawkconc-hawksq-doveconc+dovesq, level(90)

matrix study2_d_moderate = J(1,5,.)
matrix colnames study2_d_moderate = premia ll95 ul95 ll90 ul90
matrix rownames study2_d_moderate = disposition
matrix study2_d_moderate[1, 1] = .0826532*100
matrix study2_d_moderate[1, 2] = .0221091*100
matrix study2_d_moderate[1, 3] = .1431974*100
matrix study2_d_moderate[1, 4] = .0318501*100
matrix study2_d_moderate[1, 5] = .1334563*100
matrix list study2_d_moderate

*** Gender -- Trustworthy
reg trustworthy1_binary femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study2_g_trust = J(1,5,.)
matrix colnames study2_g_trust = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_trust = gender
matrix study2_g_trust[1, 1] = .042756*100
matrix study2_g_trust[1, 2] = -.044032*100
matrix study2_g_trust[1, 3] = .129544*100
matrix study2_g_trust[1, 4] = -.0300686*100
matrix study2_g_trust[1, 5] = .1155806*100
matrix list study2_g_trust

*** Party -- Trustworthy
reg trustworthy1_binary demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study2_p_trust = J(1,5,.)
matrix colnames study2_p_trust = premia ll95 ul95 ll90 ul90
matrix rownames study2_p_trust = partisan
matrix study2_p_trust[1, 1] = .0050569*100
matrix study2_p_trust[1, 2] = -.081716*100
matrix study2_p_trust[1, 3] = .0918297*100
matrix study2_p_trust[1, 4] = -.0677549*100
matrix study2_p_trust[1, 5] = .0778687*100
matrix list study2_p_trust

*** Disposition -- Trustworthy
reg trustworthy1_binary dovesq doveconc hawksq hawkconc, robust noconst
lincom hawkconc-hawksq-doveconc+dovesq, level(95)
lincom hawkconc-hawksq-doveconc+dovesq, level(90)

matrix study2_d_trust = J(1,5,.)
matrix colnames study2_d_trust = premia ll95 ul95 ll90 ul90
matrix rownames study2_d_trust = disposition
matrix study2_d_trust[1, 1] = .1017881*100
matrix study2_d_trust[1, 2] = .0166393*100
matrix study2_d_trust[1, 3] = .1869369*100
matrix study2_d_trust[1, 4] = .030339*100
matrix study2_d_trust[1, 5] = .1732372*100
matrix list study2_d_trust

*** Gender -- Tough
reg tough1_binary femsq femconc malesq maleconc, robust noconst
lincom maleconc-malesq-femconc+femsq, level(95)
lincom maleconc-malesq-femconc+femsq, level(90)

matrix study2_g_tough = J(1,5,.)
matrix colnames study2_g_tough = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_tough = gender
matrix study2_g_tough[1, 1] = -.0052062*100
matrix study2_g_tough[1, 2] = -.088058*100
matrix study2_g_tough[1, 3] = .0776456*100
matrix study2_g_tough[1, 4] = -.0747278*100
matrix study2_g_tough[1, 5] = .0643154*100
matrix list study2_g_tough

*** Party -- Tough
reg tough1_binary demsq demconc repsq repconc, robust noconst
lincom repconc-repsq-demconc+demsq, level(95)
lincom repconc-repsq-demconc+demsq, level(90)

matrix study2_p_tough = J(1,5,.)
matrix colnames study2_p_tough = premia ll95 ul95 ll90 ul90
matrix rownames study2_p_tough = partisan
matrix study2_p_tough[1, 1] = .0394769*100
matrix study2_p_tough[1, 2] = -.043391*100
matrix study2_p_tough[1, 3] = .1223448*100
matrix study2_p_tough[1, 4] = -.0300582*100
matrix study2_p_tough[1, 5] = .1090121*100
matrix list study2_p_tough

*** Disposition -- Tough
reg tough1_binary dovesq doveconc hawksq hawkconc, robust noconst
lincom hawkconc-hawksq-doveconc+dovesq, level(95)
lincom hawkconc-hawksq-doveconc+dovesq, level(90)

matrix study2_d_tough = J(1,5,.)
matrix colnames study2_d_tough = premia ll95 ul95 ll90 ul90
matrix rownames study2_d_tough = disposition
matrix study2_d_tough[1, 1] = -.0013844*100
matrix study2_d_tough[1, 2] = -.0839898*100
matrix study2_d_tough[1, 3] = .081221*100
matrix study2_d_tough[1, 4] = -.0706993*100
matrix study2_d_tough[1, 5] = .0679305*100
matrix list study2_d_tough

coefplot (matrix(study2_g_strategy[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_g_competent[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_g_moderate[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_g_trust[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_g_tough[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(11) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))), legend(off) ylabel(.67 "Policy Credibility" .83  "Competence" 1  "Moderation" 1.17  "Trustworthiness" 1.33  "Toughness", labsize(medsmall)) xlabel(-15(5)25, labsize(medium)) xmtick(-15(1)25) xtitle("Gendered Peace Premium" "(in % Points)", size(medlarge)) xline(0, lcolor(cranberry) lpatt(solid)) xvarformat(%4.1f)
graph export "${results}/figure5a.eps", replace

coefplot (matrix(study2_p_strategy[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_p_competent[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_p_moderate[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(11) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_p_trust[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(1) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_p_tough[,1]), ci((2 3) (4 5)) msymbol(S) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))), legend(off) ylabel(.67 "Policy Credibility" .83  "Competence" 1  "Moderation" 1.17  "Trustworthiness" 1.33  "Toughness", labsize(medsmall)) xlabel(-15(5)25, labsize(medium)) xmtick(-15(1)25) xtitle("Partisan Peace Premium" "(in % Points)", size(medlarge)) xline(0, lcolor(cranberry) lpatt(solid)) xvarformat(%4.1f)
graph export "${results}/figure5c.eps", replace

coefplot (matrix(study2_d_strategy[,1]), ci((2 3) (4 5)) msymbol(T) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_d_competent[,1]), ci((2 3) (4 5)) msymbol(T) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_d_moderate[,1]), ci((2 3) (4 5)) msymbol(T) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_d_trust[,1]), ci((2 3) (4 5)) msymbol(T) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_d_tough[,1]), ci((2 3) (4 5)) msymbol(T) msize(large) mfcolor(white) mlcolor(black) mlabel mlabposition(11) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))), legend(off) ylabel(.67 "Policy Credibility" .83  "Competence" 1  "Moderation" 1.17  "Trustworthiness" 1.33  "Toughness", labsize(medsmall)) xlabel(-15(5)25, labsize(medium)) xmtick(-15(1)25) xtitle("Dispositional Peace Premium" "(in % Points)", size(medlarge)) xline(0, lcolor(cranberry) lpatt(solid)) xvarformat(%4.1f)
graph export "${results}/figure5d.eps", replace

eststo clear

********************************************************************************
*				 FIGURE 5B: STUDY 2 OUT-PARTISAN MECHANISMS					   *
********************************************************************************

*** Gender -- Best Strategy

reg beststrategy1_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat political_identfication, robust noconst
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(95)
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(90)

matrix study2_g_strategy = J(1,5,.)
matrix colnames study2_g_strategy = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_strategy = gender
matrix study2_g_strategy[1, 1] = .1128574*100
matrix study2_g_strategy[1, 2] = -.0179401*100
matrix study2_g_strategy[1, 3] = .2436549*100
matrix study2_g_strategy[1, 4] = .0031041*100
matrix study2_g_strategy[1, 5] = .2226106*100
matrix list study2_g_strategy

*** Gender -- Competent

reg competent1_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat political_identfication, robust noconst
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(95)
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(90)

matrix study2_g_competent = J(1,5,.)
matrix colnames study2_g_competent = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_competent = gender
matrix study2_g_competent[1, 1] = .0281567*100
matrix study2_g_competent[1, 2] = -.1023473*100
matrix study2_g_competent[1, 3] = .1586607*100
matrix study2_g_competent[1, 4] = -.0813502*100
matrix study2_g_competent[1, 5] = .1376636*100
matrix list study2_g_competent

*** Gender -- Moderate

reg moderate 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat political_identfication, robust noconst
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(95)
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(90)

matrix study2_g_moderate = J(1,5,.)
matrix colnames study2_g_moderate = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_moderate = gender
matrix study2_g_moderate[1, 1] = -.0103264*100
matrix study2_g_moderate[1, 2] = -.1038305*100
matrix study2_g_moderate[1, 3] = .0831778*100
matrix study2_g_moderate[1, 4] = -.0887865*100
matrix study2_g_moderate[1, 5] = .0681337*100
matrix list study2_g_moderate

*** Gender -- Trustworthy

reg trustworthy1_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat political_identfication, robust noconst
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(95)
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(90)

matrix study2_g_trust = J(1,5,.)
matrix colnames study2_g_trust = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_trust = gender
matrix study2_g_trust[1, 1] = .061501*100
matrix study2_g_trust[1, 2] = -.0719685*100
matrix study2_g_trust[1, 3] = .1949705*100
matrix study2_g_trust[1, 4] = -.0504943*100
matrix study2_g_trust[1, 5] = .1734963*100
matrix list study2_g_trust

*** Gender -- Tough

reg tough1_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat political_identfication, robust noconst
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(95)
lincom (1.maleconc+1.maleconc#1.out_partisan)-(1.malesq+1.malesq#1.out_partisan)-(1.femconc+1.femconc#1.out_partisan)+(1.femsq+1.femsq#1.out_partisan), level(90)

matrix study2_g_tough = J(1,5,.)
matrix colnames study2_g_tough = premia ll95 ul95 ll90 ul90
matrix rownames study2_g_tough = gender
matrix study2_g_tough[1, 1] = .0108823*100
matrix study2_g_tough[1, 2] = -.1171717*100
matrix study2_g_tough[1, 3] = .1389364*100
matrix study2_g_tough[1, 4] = -.0965688*100
matrix study2_g_tough[1, 5] = .1183335*100
matrix list study2_g_tough

coefplot (matrix(study2_g_strategy[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_g_competent[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_g_moderate[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(11) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_g_trust[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(12) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))) (matrix(study2_g_tough[,1]), ci((2 3) (4 5)) msymbol(O) msize(large) mfcolor(black) mlcolor(black) mlabel mlabposition(1) mlabcolor(black) ciopts(lcolor(black black) lwidth(.55 1.1))), legend(off) ylabel(.67 "Policy Credibility" .83  "Competence" 1  "Moderation" 1.17  "Trustworthiness" 1.33  "Toughness", labsize(medsmall)) xlabel(-20(5)30, labsize(medium)) xmtick(-20(1)30) xtitle("Out-Partisan Gendered Peace Premium" "(in % Points)", size(medlarge)) xline(0, lcolor(cranberry) lpatt(solid)) xvarformat(%4.1f)
graph export "${results}/figure5b.eps", replace

********************************************************************************
*							 TABLE 4: STUDY 2 SUCCESS						   *
********************************************************************************

*Gender -- Binary DV
reg disapproval2_binary femsq femconc malesq maleconc, robust noconst
lincom femconc-femsq-maleconc+malesq

*Gender -- Full DV
reg disapproval2 femsq femconc malesq maleconc, robust noconst
lincom femconc-femsq-maleconc+malesq	
	
*Gender -- Passed Manipulation Check / Binary DV
reg disapproval2_binary femsq femconc malesq maleconc if policy_manipcheck==1 & name_manipcheck==1, robust noconst
lincom femconc-femsq-maleconc+malesq

*Gender -- Passed Manipulation Check / Full DV
reg disapproval2 femsq femconc malesq maleconc if policy_manipcheck==1 & name_manipcheck==1, robust noconst
lincom femconc-femsq-maleconc+malesq

*Gender Out-Partisan -- Binary DV
reg disapproval2_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

*Gender Out-Partisan -- Full DV
reg disapproval2 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)
	
*Gender Out-Partisan -- Passed Manipulation Check / Binary DV
reg disapproval2_binary 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo if policy_manipcheck==1 & name_manipcheck==1, robust noconst
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

*Gender Out-Partisan -- Passed Manipulation Check / Full DV
reg disapproval2 1.femsq 1.femsq#out_partisan 1.femconc 1.femconc#out_partisan 1.malesq 1.malesq#out_partisan 1.maleconc 1.maleconc#out_partisan democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo if policy_manipcheck==1 & name_manipcheck==1, robust noconst
lincom (1.femconc+1.femconc#1.out_partisan)-(1.femsq+1.femsq#1.out_partisan)-(1.maleconc+1.maleconc#1.out_partisan)+(1.malesq+1.malesq#1.out_partisan)

*Partisan -- Binary DV
reg disapproval2_binary demsq demconc repsq repconc, robust noconst
lincom demconc-demsq-repconc+repsq

*Partisan -- Full DV
reg disapproval2 demsq demconc repsq repconc, robust noconst
lincom demconc-demsq-repconc+repsq	
	
*Partisan -- Passed Manipulation Check / Binary DV
reg disapproval2_binary demsq demconc repsq repconc if policy_manipcheck==1, robust noconst
lincom demconc-demsq-repconc+repsq	

*Partisan -- Passed Manipulation Check / Full DV
reg disapproval2 demsq demconc repsq repconc if policy_manipcheck==1, robust noconst
lincom demconc-demsq-repconc+repsq	

*Disposition -- Binary DV
reg disapproval2_binary dovesq doveconc hawksq hawkconc, robust noconst
lincom doveconc-dovesq-hawkconc+hawksq

*Disposition -- Full DV
reg disapproval2 dovesq doveconc hawksq hawkconc, robust noconst
lincom doveconc-dovesq-hawkconc+hawksq	
	
*Disposition -- Passed Manipulation Check / Binary DV
reg disapproval2_binary dovesq doveconc hawksq hawkconc if policy_manipcheck==1 & hawkdove_manipcheck==1, robust noconst
lincom doveconc-dovesq-hawkconc+hawksq

*Disposition -- Passed Manipulation Check / Full DV
reg disapproval2 dovesq doveconc hawksq hawkconc if policy_manipcheck==1 & hawkdove_manipcheck==1, robust noconst
lincom doveconc-dovesq-hawkconc+hawksq

eststo clear

********************************************************************************
*					 TABLE A-10: STUDY 2 7-POINT SCALE						   *
********************************************************************************

***Main Effect of Gender -- Outcome 1, Full DV
reg disapproval1 femsq femconc malesq maleconc, robust noconst
lincom femconc-femsq
lincom maleconc-malesq
lincom femconc-femsq-maleconc+malesq

*** Main Effect of Partisanship -- Outcome 1, Full DV
reg disapproval1 demsq demconc repsq repconc, robust noconst
lincom demconc-demsq
lincom repconc-repsq
lincom demconc-demsq-repconc+repsq

*** Main Effect of Disposition -- Outcome 1, Full DV
reg disapproval1 dovesq doveconc hawksq hawkconc, robust noconst
lincom doveconc-dovesq
lincom hawkconc-hawksq
lincom doveconc-dovesq-hawkconc+hawksq

********************************************************************************
*					 TABLE A-11: STUDY 2 MANIPULATION CHECK					   *
********************************************************************************

***Main Effect of Gender -- Outcome 1, Binary DV
reg disapproval1_binary femsq femconc malesq maleconc if policy_manipcheck==1 & name_manipcheck==1, robust noconst
lincom femconc-femsq
lincom maleconc-malesq
lincom femconc-femsq-maleconc+malesq

*** Main Effect of Partisanship -- Outcome 1, Binary DV
reg disapproval1_binary demsq demconc repsq repconc if policy_manipcheck==1, robust noconst
lincom demconc-demsq
lincom repconc-repsq
lincom demconc-demsq-repconc+repsq

*** Main Effect of Disposition -- Outcome 1, Binary DV
reg disapproval1_binary dovesq doveconc hawksq hawkconc if policy_manipcheck==1 & hawkdove_manipcheck==1, robust noconst
lincom doveconc-dovesq
lincom hawkconc-hawksq
lincom doveconc-dovesq-hawkconc+hawksq

********************************************************************************
*						 TABLE A-12: STUDY 2 WITH COVARIATES					   *
********************************************************************************

eststo clear

*Model 1: Main Effect of Gender -- Outcome 1, Binary DV
eststo: reg disapproval1_binary femsq femconc malesq maleconc democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom femconc-femsq-maleconc+malesq
	
*Model 2: Main Effect of Gender -- Outcome 1, Full DV
eststo: reg disapproval1 femsq femconc malesq maleconc democrat dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom femconc-femsq-maleconc+malesq

*Model 3: Main Effect of Partisanship -- Outcome 1, Binary DV
eststo: reg disapproval1_binary demsq demconc repsq repconc female dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom demconc-demsq-repconc+repsq
	
*Model 4: Main Effect of Partisanship -- Outcome 1, Full DV
eststo: reg disapproval1 demsq demconc repsq repconc female dove hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom demconc-demsq-repconc+repsq

*Model 5: Main Effect of Disposition -- Outcome 1, Binary DV
eststo: reg disapproval1_binary hawksq hawkconc dovesq doveconc female democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom doveconc-dovesq-hawkconc+hawksq
	
*Model 6: Main Effect of Disposition -- Outcome 1, Full DV
eststo: reg disapproval1 hawksq hawkconc dovesq doveconc female democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst
lincom doveconc-dovesq-hawkconc+hawksq

esttab using "${results}/table_a12.tex", cells(b(fmt(3)) ci(fmt(3) par)) noeqlines eqlabels(none) nogaps se varlabels(demsq "Democratic x Status Quo" demconc "Democratic x Conciliatory" repsq "Republican x Status Quo" repconc "Republican x Conciliatory" femsq "Female x Status Quo" femconc "Female x Conciliatory" malesq "Male x Status Quo" maleconc "Male x Conciliatory" dovesq "Dovish x Status Quo" doveconc "Dovish x Conciliatory" hawksq "Hawkish x Status Quo" hawkconc "Hawkish x Conciliatory" democrat "Democratic President" female "Female President" dove "Dovish President") keep(malesq maleconc femsq femconc repsq repconc demsq demconc hawksq hawkconc dovesq doveconc female democrat dove) order(malesq maleconc femsq femconc repsq repconc demsq demconc hawksq hawkconc dovesq doveconc female democrat dove) label star(* 0.10 ** 0.05 *** .01) nonotes mtitle("Disapproval (Binary)" "Disapproval (7-Point)" "Disapproval (Binary)" "Disapproval (7-Point)" "Disapproval (Binary)" "Disapproval (7-Point)") b(3) se(3) replace
eststo clear

********************************************************************************
*					 TABLE A-13: STUDY 2 GENDER X DEM PRESIDENT				   *
********************************************************************************

eststo clear

***Model 1: Binary DV, Full Sample
eststo: reg disapproval1_binary 1.femsq 1.femsq#democrat 1.femconc 1.femconc#democrat 1.malesq 1.malesq#democrat 1.maleconc 1.maleconc#democrat, robust noconst

*Gendered Peace Premium (Democratic President)
lincom (1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat)

*Gendered Peace Premium (Non-Democratic President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 2: 7-Point DV, Full Sample
eststo: reg disapproval1 1.femsq 1.femsq#democrat 1.femconc 1.femconc#democrat 1.malesq 1.malesq#democrat 1.maleconc 1.maleconc#democrat, robust noconst

*Gendered Peace Premium (Democratic President)
lincom (1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat)

*Gendered Peace Premium (Non-Democratic President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 3: Binary DV, Attentive Sample
eststo: reg disapproval1_binary 1.femsq 1.femsq#democrat 1.femconc 1.femconc#democrat 1.malesq 1.malesq#democrat 1.maleconc 1.maleconc#democrat if policy_manipcheck==1 & name_manipcheck==1, robust noconst

*Gendered Peace Premium (Democratic President)
lincom (1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat)

*Gendered Peace Premium (Non-Democratic President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 4: 7-Point DV, Attentive Sample
eststo: reg disapproval1 1.femsq 1.femsq#democrat 1.femconc 1.femconc#democrat 1.malesq 1.malesq#democrat 1.maleconc 1.maleconc#democrat if policy_manipcheck==1 & name_manipcheck==1, robust noconst

*Gendered Peace Premium (Democratic President)
lincom (1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat)

*Gendered Peace Premium (Non-Democratic President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.democrat)-(1.femsq+1.femsq#1.democrat)-(1.maleconc+1.maleconc#1.democrat)+(1.malesq+1.malesq#1.democrat))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

eststo clear

********************************************************************************
*				 TABLE A-14: STUDY 2 GENDER X DISP PRESIDENT				   *
********************************************************************************

eststo clear

***Model 1: Binary DV, Full Sample
eststo: reg disapproval1_binary 1.femsq 1.femsq#hawk 1.femconc 1.femconc#hawk 1.malesq 1.malesq#hawk 1.maleconc 1.maleconc#hawk, robust noconst

*Gendered Peace Premium (Hawkish President)
lincom (1.femconc+1.femconc#1.hawk)-(1.femsq+1.femsq#1.hawk)-(1.maleconc+1.maleconc#1.hawk)+(1.malesq+1.malesq#1.hawk)

*Gendered Peace Premium (Dovish President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.hawk)-(1.femsq+1.femsq#1.hawk)-(1.maleconc+1.maleconc#1.hawk)+(1.malesq+1.malesq#1.hawk))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 2: 7-Point DV, Full Sample
eststo: reg disapproval1 1.femsq 1.femsq#hawk 1.femconc 1.femconc#hawk 1.malesq 1.malesq#hawk 1.maleconc 1.maleconc#hawk, robust noconst

*Gendered Peace Premium (Hawkish President)
lincom (1.femconc+1.femconc#1.hawk)-(1.femsq+1.femsq#1.hawk)-(1.maleconc+1.maleconc#1.hawk)+(1.malesq+1.malesq#1.hawk)

*Gendered Peace Premium (Dovish President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.hawk)-(1.femsq+1.femsq#1.hawk)-(1.maleconc+1.maleconc#1.hawk)+(1.malesq+1.malesq#1.hawk))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 3: Binary DV, Attentive Sample
eststo: reg disapproval1_binary 1.femsq 1.femsq#hawk 1.femconc 1.femconc#hawk 1.malesq 1.malesq#hawk 1.maleconc 1.maleconc#hawk if policy_manipcheck==1 & name_manipcheck==1 & hawkdove_manipcheck==1, robust noconst

*Gendered Peace Premium (Hawkish President)
lincom (1.femconc+1.femconc#1.hawk)-(1.femsq+1.femsq#1.hawk)-(1.maleconc+1.maleconc#1.hawk)+(1.malesq+1.malesq#1.hawk)

*Gendered Peace Premium (Dovish President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.hawk)-(1.femsq+1.femsq#1.hawk)-(1.maleconc+1.maleconc#1.hawk)+(1.malesq+1.malesq#1.hawk))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

***Model 4: 7-Point DV, Attentive Sample
eststo: reg disapproval1 1.femsq 1.femsq#hawk 1.femconc 1.femconc#hawk 1.malesq 1.malesq#hawk 1.maleconc 1.maleconc#hawk if policy_manipcheck==1 & name_manipcheck==1 & hawkdove_manipcheck==1, robust noconst

*Gendered Peace Premium (Hawkish President)
lincom (1.femconc+1.femconc#1.hawk)-(1.femsq+1.femsq#1.hawk)-(1.maleconc+1.maleconc#1.hawk)+(1.malesq+1.malesq#1.hawk)

*Gendered Peace Premium (Dovish President)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.hawk)-(1.femsq+1.femsq#1.hawk)-(1.maleconc+1.maleconc#1.hawk)+(1.malesq+1.malesq#1.hawk))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

eststo clear


********************************************************************************
*				 TABLE A-15: STUDY 2 DISPOSITION MEDIATION 					   *
********************************************************************************

eststo clear

***** COMPETENCE ***** 

***Hawk
medeff (regress beststrategy1 conciliatory female democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 beststrategy1 conciliatory female democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if hawk==1, mediate(beststrategy1) treat(conciliatory) sims(2000) seed(8675309)

***Doves	
medeff (regress beststrategy1 conciliatory female democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 beststrategy1 conciliatory female democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if dove==1, mediate(beststrategy1) treat(conciliatory) sims(2000) seed(8675309)

***** MODERATION ***** 

***Hawk
medeff (regress moderate conciliatory female democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 moderate conciliatory female democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if hawk==1, mediate(moderate) treat(conciliatory) sims(2000) seed(8675309)

***Doves	
medeff (regress moderate conciliatory female democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 moderate conciliatory female democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if dove==1, mediate(moderate) treat(conciliatory) sims(2000) seed(8675309)

***** TRUSTWORTHINESS ***** 

***Hawk
medeff (regress trustworthy1 conciliatory female democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 trustworthy1 conciliatory female democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if hawk==1, mediate(trustworthy1) treat(conciliatory) sims(2000) seed(8675309)

***Doves	
medeff (regress trustworthy1 conciliatory female democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 trustworthy1 conciliatory female democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if dove==1, mediate(trustworthy1) treat(conciliatory) sims(2000) seed(8675309)


********************************************************************************
*			 TABLE A-16: STUDY 2 GENDER OUT-PARTISAN MEDIATION 				   *
********************************************************************************

eststo clear

***** COMPETENCE ***** 

***Male
medeff (regress beststrategy1 conciliatory hawk democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 beststrategy1 conciliatory hawk democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if male==1 & out_partisan==1, mediate(beststrategy1) treat(conciliatory) sims(2000) seed(8675309)

***Female
medeff (regress beststrategy1 conciliatory hawk democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) (regress disapproval1 beststrategy1 conciliatory hawk democrat hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo) if female==1 & out_partisan==1, mediate(beststrategy1) treat(conciliatory) sims(2000) seed(8675309)


********************************************************************************
*				 TABLE A-17: STUDY 2 HETEROGENEOUS EFFECTS					   *
********************************************************************************

eststo clear

***Model 1: Republican Respondent
eststo: reg disapproval1 1.femsq 1.femsq#republican_respondent 1.femconc 1.femconc#republican_respondent 1.malesq 1.malesq#republican_respondent 1.maleconc 1.maleconc#republican_respondent hawk democrat hostsexism benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Republican Respondent)
lincom (1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent)

*Gendered Peace Premium (Non-Republican Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.republican_respondent)-(1.femsq+1.femsq#1.republican_respondent)-(1.maleconc+1.maleconc#1.republican_respondent)+(1.malesq+1.malesq#1.republican_respondent))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 2: Hostile Sexism 
eststo: reg disapproval1 1.femsq 1.femsq#hostsexismIQR 1.femconc 1.femconc#hostsexismIQR 1.malesq 1.malesq#hostsexismIQR 1.maleconc 1.maleconc#hostsexismIQR hawk democrat republican_respondent benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Sexist Respondent)
lincom (1.femconc+1.femconc#1.hostsexismIQR)-(1.femsq+1.femsq#1.hostsexismIQR)-(1.maleconc+1.maleconc#1.hostsexismIQR)+(1.malesq+1.malesq#1.hostsexismIQR)

*Gendered Peace Premium (Non-Sexist Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.hostsexismIQR)-(1.femsq+1.femsq#1.hostsexismIQR)-(1.maleconc+1.maleconc#1.hostsexismIQR)+(1.malesq+1.malesq#1.hostsexismIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 3: Benevolent Sexism 
eststo: reg disapproval1 1.femsq 1.femsq#benevsexismIQR 1.femconc 1.femconc#benevsexismIQR 1.malesq 1.malesq#benevsexismIQR 1.maleconc 1.maleconc#benevsexismIQR hawk democrat republican_respondent hostsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Sexist Respondent)
lincom (1.femconc+1.femconc#1.benevsexismIQR)-(1.femsq+1.femsq#1.benevsexismIQR)-(1.maleconc+1.maleconc#1.benevsexismIQR)+(1.malesq+1.malesq#1.benevsexismIQR)

*Gendered Peace Premium (Non-Sexist Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.benevsexismIQR)-(1.femsq+1.femsq#1.benevsexismIQR)-(1.maleconc+1.maleconc#1.benevsexismIQR)+(1.malesq+1.malesq#1.benevsexismIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 4: Second-Order Sexism 
eststo: reg disapproval1 1.femsq 1.femsq#secordersexismIQR 1.femconc 1.femconc#secordersexismIQR 1.malesq 1.malesq#secordersexismIQR 1.maleconc 1.maleconc#secordersexismIQR hawk democrat republican_respondent hostsexism benevsexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Sexist Respondent)
lincom (1.femconc+1.femconc#1.secordersexismIQR)-(1.femsq+1.femsq#1.secordersexismIQR)-(1.maleconc+1.maleconc#1.secordersexismIQR)+(1.malesq+1.malesq#1.secordersexismIQR)

*Gendered Peace Premium (Non-Sexist Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.secordersexismIQR)-(1.femsq+1.femsq#1.secordersexismIQR)-(1.maleconc+1.maleconc#1.secordersexismIQR)+(1.malesq+1.malesq#1.secordersexismIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 5: Militant Assertiveness 
eststo: reg disapproval1 1.femsq 1.femsq#hawkishIQR 1.femconc 1.femconc#hawkishIQR 1.malesq 1.malesq#hawkishIQR 1.maleconc 1.maleconc#hawkishIQR hawk democrat republican_respondent hostsexism benevsexism secordersexism female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Hawkish Respondent)
lincom (1.femconc+1.femconc#1.hawkishIQR)-(1.femsq+1.femsq#1.hawkishIQR)-(1.maleconc+1.maleconc#1.hawkishIQR)+(1.malesq+1.malesq#1.hawkishIQR)

*Gendered Peace Premium (Non-Hawkish Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.hawkishIQR)-(1.femsq+1.femsq#1.hawkishIQR)-(1.maleconc+1.maleconc#1.hawkishIQR)+(1.malesq+1.malesq#1.hawkishIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 6: Education 
eststo: reg disapproval1 1.femsq 1.femsq#educationIQR 1.femconc 1.femconc#educationIQR 1.malesq 1.malesq#educationIQR 1.maleconc 1.maleconc#educationIQR hawk democrat republican_respondent hostsexism benevsexism secordersexism hawkish female_respondent political_identfication hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Educated Respondent)
lincom (1.femconc+1.femconc#1.educationIQR)-(1.femsq+1.femsq#1.educationIQR)-(1.maleconc+1.maleconc#1.educationIQR)+(1.malesq+1.malesq#1.educationIQR)

*Gendered Peace Premium (Uneducated Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.educationIQR)-(1.femsq+1.femsq#1.educationIQR)-(1.maleconc+1.maleconc#1.educationIQR)+(1.malesq+1.malesq#1.educationIQR))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))


***Model 7: Female Respondents
eststo: reg disapproval1 1.femsq 1.femsq#female_respondent 1.femconc 1.femconc#female_respondent 1.malesq 1.malesq#female_respondent 1.maleconc 1.maleconc#female_respondent hawk democrat republican_respondent hostsexism benevsexism secordersexism hawkish political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Gendered Peace Premium (Female Respondent)
lincom (1.femconc+1.femconc#1.female_respondent)-(1.femsq+1.femsq#1.female_respondent)-(1.maleconc+1.maleconc#1.female_respondent)+(1.malesq+1.malesq#1.female_respondent)

*Gendered Peace Premium (Male Respondent)
lincom (1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq)

*Difference 
lincom ((1.femconc+1.femconc#1.female_respondent)-(1.femsq+1.femsq#1.female_respondent)-(1.maleconc+1.maleconc#1.female_respondent)+(1.malesq+1.malesq#1.female_respondent))-((1.femconc)-(1.femsq)-(1.maleconc)+(1.malesq))

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*					 TABLE A-18: STUDY 2 OTHER HETEROGENEITY				   *
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eststo clear

***Model 1: Sexists and Women Leaders

eststo: regress disapproval1 i.female##c.hostsexism conciliatory democrat hawk benevsexism secordersexism hawkish female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust
eststo clear

***Model 2: Hawks and Conciliation

eststo: regress disapproval1 i.conciliatory##c.hawkish female democrat hawk hostsexism benevsexism secordersexism female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust
eststo clear

***Model 3: Republicans and Conciliation

eststo: regress disapproval1 i.conciliatory##c.political_identfication female democrat hawk hostsexism benevsexism secordersexism hawkish female_respondent education hhi age white SexismOrder nonwhite_placebo, robust
eststo clear

***Model 4: Women and Women Leaders

eststo: regress disapproval1 i.female##i.female_respondent conciliatory democrat hawk hostsexism benevsexism secordersexism hawkish political_identfication education hhi age white SexismOrder nonwhite_placebo, robust
eststo clear

***Model 5: Hawks and Hawks Advantage

eststo: regress disapproval1 i.dovesq##i.hawkishIQR i.doveconc##i.hawkishIQR i.hawksq##i.hawkishIQR female democrat hostsexism benevsexism secordersexism female_respondent political_identfication education hhi age white SexismOrder nonwhite_placebo, robust noconst

*Dispositional Peace Premium (High Militant Assertiveness)
lincom (1.doveconc+1.doveconc#1.hawkishIQR)-(1.dovesq+1.dovesq#1.hawkishIQR)+(1.hawksq+1.hawksq#1.hawkishIQR)

*Dispositional Peace Premium (Low Militant Assertiveness)
lincom 1.doveconc-1.dovesq+1.hawksq	

*Difference 
lincom ((1.doveconc+1.doveconc#1.hawkishIQR)-(1.dovesq+1.dovesq#1.hawkishIQR)+(1.hawksq+1.hawksq#1.hawkishIQR)) - ((1.doveconc-1.dovesq+1.hawksq))

eststo clear


clear

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